Argumentative relation classification with background knowledge

Debjit, Paul ; Opitz, Juri ; Becker, Maria ; Kobbe, Jonathan ; Hirst, Graeme ; Frank, Anette

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Document Type: Conference or workshop publication
Year of publication: 2020
Book title: Computational models of argument : proceedings of COMMA 2020
The title of a journal, publication series: Frontiers in Artificial Intelligence and Applications
Volume: 326
Page range: 319-330
Conference title: 8th COMMA 2020
Location of the conference venue: Perugia, Italy
Date of the conference: 04.-11.09.2020
Publisher: Prakken, Henry
Place of publication: Amsterdam
Publishing house: IOS Press
ISBN: 978-1-64368-106-1 , 978-1-64368-107-8
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Abstract: A common conception is that the understanding of relations that hold between argument units requires knowledge beyond the text. But to date, argument analysis systems that leverage knowledge resources are still very rare. In this paper, we propose an unsupervised graph-based ranking method that extracts relevant multi-hop knowledge from a background knowledge resource. This knowledge is integrated into a neural argumentative relation classifier via an attention-based gating mechanism. In contrast to prior work we emphasize the selection of relevant multi-hop knowledge, and apply methods to automatically enrich the knowledge resource with missing knowledge. We assess model performance on two datasets, showing considerable improvement over strong baselines.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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